Digital images are used in current multimedia devices. A function among basic functions of the multimedia device determines the existence and location of a face in a digital image. The function is needed in the case of sorting images stored in the device according to contents, processing an image region by a digital photographing and printing device, identifying and verifying in an access control and video surveillance system, interacting with a person with a computer system, and others.
In order to solve the object of detecting a task in an image, there are many techniques using a neural network, vector decomposition, a support vector machine (SVM), and others. Under the condition in which an object to be searched is not distinctly formalized, these approaches use a training stage (e.g. parameter tuning) that needs a large number of samples for the object. The training stage in each approach performs a task for determining an object grade in an image, which requires a large amount of computation and a high cost arises therefrom. Computational complexity significantly increases when a location, size, and direction of a face in an image are determined in the training stage.
There are face detection systems described in U.S. Pat. Nos. 6,661,907 and 6,816,611. The systems use color information of images. This peculiarity significantly restricts areas in which the method is applied to because of requiring a color image capturing device.
In addition, there is a two-stage face detection system described in U.S. Pat. No. 6,940,545. The system is based on a probabilistic model estimating color information related to the head of a person, for example, hair and skin, in the first stage, and uses a Bayesian classifier in the second stage. The Bayesian classifier processes a hypothesis and performs a final decision about the existence and location of a face in an image. This system may be embedded in a digital camera for precise estimation for image capturing parameters when a face exists in the area to be photographed. However, this system induces quite weak requirements on algorithm efficiency and processing speed, and thereby it is apparently inefficient in many other face detection tasks.
Another system using a two-stage face detection algorithm is disclosed in U.S. Pat. No. 6,463,163. In the system, a two-element algorithm including liner and nonlinear filters is performed in the first stage. Correlation with a core of the linear filter is first calculated and then the resulted correlation map is processed in order to extract local extremes. The first stage is completed by comparing intensity characteristics of regions related to the extremes with values obtained from a model. Through the first stage, a set of regions where a face could be located is obtained. At the second stage, the found regions are processed by a multilayer feed-forward neural network, and thereby the list of faces found in images is obtained. However, the algorithm has drawbacks in that the stability of face orientation is low. Further, the computational speed of the multilayer neural networks is quite low, and therefore it could be insufficient for running the algorithm in real-time applications
These drawbacks were partly solved in U.S. Pat. No. 7,099,510. It proposes an algorithm for effectively searching a location of a face region with computation considering shifting and scale adjusting. The algorithm is based on a cascade of simple classification procedures. The construction and combinations of classifiers according to the cascade result in high accuracy of tasks and low running time. However, the face detection effectiveness of all the classifiers is quite low.
As stated above, in the prior systems, high processing speed in detecting a face is needed. Further, due to errors occurred in detecting a face occur, the factors (e.g. an obstacle such as diversity of faces, spectacles, mustache, or a hat) having a big effect on the performance of the system are not processed. In addition, structural complexity of external environments, randomness of illumination, and others result in many errors such as detecting a non-existent face in practice. These errors are fatal to the performance of a biometric identification system.